Sustainable Development Goals (SDG) achievement in North-East India: a district-level analysis

With a vision to preserve the planet and foster human prosperity, the United Nations Sustainable Development Goals (SDGs) have come up with an inclusive and renewed global agenda to address persistent challenges that transcend geographical boundaries. In contrast to former development agendas that prominently emphasised economic growth, the SDG constitute a universal framework that incorporates a broad range of potentially divergent aspects across economic, social and environmental dimensions. While certain goals reflect a strong mutually reinforcing connection, others reflect an intrinsic trade-off, which makes it pivotal to enhance synergies and effectively manage conflicts to ensure their holistic achievement. As reported in the UN SDGs Report, India occupies the 109th position out of 193 UN member nations, with an overall SDG achievement score of 63.99 (Sachs et al., 2024a, b). India, being a vast country, witnessed substantial variations in socio-economic development across districts and regions, highlighting uneven progress and disparities within and between different areas (Ohlan, 2013). Similarly, in terms of the spillover index, which captures the extent to which a country's actions positively influence other nations' ability to achieve the SDG, India holds the 27th position among 193 countries (Sachs et al., 2024a, b). The country's National Development Agenda is closely aligned with the SDG, and notable progress has been documented in several domains. For instance, Biswas et al. (2024) highlight substantial improvements in SDG 6 on clean water and Sanitation, while the study also underscored the considerable regional disparities across states in terms of achievement levels. To advance the SDGs, the Government of India established the National Institution for Transforming India (NITI Aayog) to coordinate the implementation of SDG as well as to track the progress (Ghosh and Chakravarty, 2023). In order to track the progress systematically, NITI Aayog developed the SDG India Index. Despite significant progress in the achievement of SDG, studies found some persistent regional disparities rooted in governance, resource allocation, historical legacies and socio-cultural contexts, which continue to impede the uniform achievement of the SDG in India. Therefore, addressing these disparities is imperative to accelerate the SDG achievement and realise a vision of inclusive, sustainable growth (Saha et al., 2023).

To shed light on this vital topic, contemporary research is beginning to examine the interlinkage among the 17 SDGs (Pradhan et al., 2017; Bruer et al., 2019). Although numerous studies have been undertaken on SDG, very limited studies have been conducted in India, specifically in the context of the North East. An inclusive review of pertinent literature revealed that limited research has been undertaken to conduct a district-level analysis with regard to SDG achievement in the NER of India. The NER of India has distinct socio-cultural identities, ecological richness and developmental characteristics. The distinctive socio-cultural and ecological characteristic of this region offers an optimal context for a focused and region-specific study on this crucial topic. However, despite its significance, research on SDG achievements, specifically at the district level, remains scarce, which creates a substantial knowledge gap that demands urgent scholarly attention.

The study on the progress and disparities in achieving the SDG across various districts of the North-Eastern Region (NER) of India is motivated by various factors. The NER, often called the rainbow of the country, is a land of unique ecological richness and cultural diversity. Comprising eight states, Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura, this region accounts for nearly 7.98% of India's total geographical area and 3.79% of the total population. Moreover, almost 96% of the NER shares an international border with China, Bhutan, Myanmar, Nepal and Bangladesh, which makes it a geopolitically sensitive and strategically vital frontier for India's foreign relations. Despite its immense resources and wealth, the region remains at the lowest pedestal of structural and economic development, with a predominantly rural economy where more than 84% of the population resides in villages. Recognised as a global biodiversity hotspot, the region sustains nearly 52% of its land under forest cover. This ecological wealth provides global public goods, such as climate regulation and biodiversity preservation. In recent years, the NER has reflected significant progress in achieving several SDGs, notably poverty alleviation (SDG 1), health and well-being (SDG 3), quality education (SDG 4) and decent work (SDG 8). However, its unique geographical, social and economic context makes the pace and distribution of this progress highly uneven across states and districts. Therefore, the motivation to investigate progress and disparities in achieving the SDG across various districts of North East India stems from the following key factors: First, while national and state-level parameters of SDG progress are available, they often mask the deep intra-regional disparities within districts. Therefore, a district-level analysis is crucial to unveil the localised development challenges and opportunities. Second, the NER's socio-cultural heterogeneity and geographically diverse terrain demand context and need specific policies to help in this vibrant region's holistic development. Third, given the area's strategic significance and its proximity to international borders, balanced and inclusive development within districts is not only a developmental priority but also a matter of national security and stability. Fourth, the region's ecological diversity demands a more comprehensive analysis of how districts are progressing toward environmentally linked SDG, since unobserved disparities could undermine both livelihoods and ecological sustainability. Finally, district-level insights are vital for indication-based policymaking, which enables governments and development partners to design targeted strategies that address pockets of deprivation while reinforcing areas of progress.

The present study enriches the existing literature in the following way. At first, it has significantly contributed to the debate on the progress and disparities in achieving SDG across various districts of this diverse region. Secondly, this study stands out by focusing precisely on NER, and to the best of the authors' knowledge, it is the first-ever empirical examination focusing on the progress and disparities of various districts of NER in achieving SDG. While prior research examines the SDG progress at the state or national level, this study makes a unique contribution by uncovering the intra-regional disparities and localised development gaps across districts, which offers a more granular understanding of progress. The study highlights the region-specific challenges and opportunities by focusing on a geographically strategic and socio-economically distinct region. Thus, it fills a critical knowledge gap and contributes to evidence-based policymaking for inclusive and balanced development.

Therefore, the current study endeavours to investigate progress and disparities in achieving the SDG across various districts of the NER of India, which has not been adequately investigated in earlier literature.

The subsequent sections of the study have been framed as follows: Section 2 presents the literature review related to the topic. Section 3 describes the research methodology. Section 4 outlines the empirical results and discussions. Section 5 provides the conclusion and policy implications.

The analytical edifice of the present study is grounded in the multidimensional paradigm of sustainable development advanced by the United Nations through the 2030 Agenda, which redefines development as an integrated process encompassing economic, social and environmental dimensions rather than a purely growth-centric phenomenon (Sachs et al., 2022a, b). This paradigm is theoretically anchored in the principles of Integrated Sustainable Development and systems theory, which conceptualize development as a complex adaptive system characterized by non-linearity, feedback mechanisms and dynamic interdependencies among constituent subsystems (Pradhan et al., 2017; Nilsson et al., 2016). Within this analytical construct, the SDG are understood as a densely interconnected network of targets exhibiting both synergistic complementarities and inherent trade-offs, wherein progress in one domain may either catalyze or constrain advancements in others (Le Blanc, 2015; Miola and Schiltz, 2019; Zhou et al., 2022). Such interdependencies acquire heightened significance in ecologically fragile and socio-culturally heterogeneous regions such as North-East India, where development interventions are embedded within intricate socio-ecological systems and often yield spatially uneven and context-contingent outcomes. Complementing this, spatial development theory (Friedmann, 1966), alongside its evolution into place-based policy frameworks (Barca et al., 2012), provides a critical lens by demonstrating that aggregate national or state-level indicators frequently obscure profound intra-regional disparities, thereby necessitating analytically disaggregated, district-level investigations for more precise diagnosis and targeted policy formulation. Recent empirical literature further accentuates that SDG performance is not merely a function of resource endowments but is significantly mediated by governance capacity, institutional quality and infrastructural adequacy, thereby reinforcing the centrality of decentralized and context-sensitive analytical frameworks (Kumar et al., 2016; Zhou et al., 2022; De and Devi, 2023; Debnath et al., 2025). Accordingly, by synthesizing insights from systems thinking, SDG interlinkage theory and spatial development perspectives, the present study advances a district-level analytical framework that captures the inherently multidimensional, interdependent and spatially differentiated nature of sustainable development, thereby addressing a critical lacuna in the extant literature and enabling a more nuanced and empirically grounded understanding of SDG achievement in the NER of India (Sachs et al., 2022a, b).

The pursuit of the SDGs in NER of India represents a complex intersection of ecological preservation, ethnic diversity and distinct developmental bottlenecks. The global 2030 Agenda, as established by the United Nations, provides a universal framework for sustainable growth; however, its implementation in the Indian context is overseen by NITI Aayog, which has adapted these global targets into national and sub-national indicators. Scholars argue that the success of the global SDG is inextricably linked to India's progress, given its demographic weight and socio-economic diversity (Sachs et al., 2024a, b). Within this national framework, NER of India is often categorised as a “frontier region,” where geographical isolation and the challenges of the “Chicken's Neck” corridor create unique hurdles for infrastructure-related goals, specifically SDG 9 (Haokip, 2011). Purkarthofer (2023) highlights that regional planning and regional perspectives more generally play a crucial role in the strive for ecological, social and economic sustainability. Bhattacharya (2024) further emphasised policy intervention for the overall development of NER and effective utilisation of local resources.

A critical transition in recent academic discourse is the shift from state-level monitoring to the localisation of SDG at the district level. While state-level indices offer a broad overview of developmental trajectories, they often obscure significant intra-state disparities. The introduction of the NER District SDG Index by NITI Aayog and the UNDP has facilitated a more granular analysis, allowing researchers to investigate why certain districts in states like Mizoram or Sikkim consistently outperform their counterparts in Nagaland or Arunachal Pradesh despite sharing similar topographical constraints.

Sectoral analysis within the literature reveals a nuanced picture of progress across the region. In terms of health and poverty (SDGs 1, 2 and 3), while absolute poverty rates are lower in many NE districts compared to central India, multidimensional poverty remains a persistent issue due to inadequate healthcare access in rugged terrains. Furthermore, while the region is celebrated for high literacy rates (SDG 4), Bhattacharya (2024) documented a significant disconnect between educational attainment and employability, particularly in districts lagging in digital infrastructure and vocational training. Additionally, the region's reputation for gender-progressive social structures – driven by the matrilineal traditions of groups like the Khasis – is often challenged by empirical data showing a lack of formal political representation for women, highlighting a gap in achieving SDG 5. Singh (2018) investigated the impact of different policy initiatives which in turns caused regional disparities in NES.

Despite the growing body of work on the North-East's development, a significant research gap persists. Much of the existing studies remain focused on either broad state-level comparisons or narrow thematic studies, such as organic farming in Sikkim or insurgency in Manipur. There is a scarcity of comprehensive, cross-border analysis that treats the district as the primary unit of observation across all eight states simultaneously. By focusing on a district-level analysis, this study seeks to identify specific “lagging clusters” and “pockets of excellence,” providing a data-driven foundation for localised policy interventions that are often overlooked in centralised planning.

The United Nations' 2030 Agenda and the SDGs reflect a paradigm shift from economic-centric development to a multi-dimensional model encompassing economic, social and environmental sustainability (Sachs et al., 2024a, b). This shift is grounded in the theory of Integrated Sustainable Development, which emphasises synergies and trade-offs among different goals (Nilsson et al., 2016). The framework operates within a systems theory approach, where development is non-linear, interdependent and context-driven (Pradhan et al., 2017). Studies such as Le Blanc (2015) and Griggs et al. (2013) argue for the need to understand SDG as interlinked policy goals, where progress in one goal may reinforce or hinder another. This is especially critical in ecologically fragile and socio-culturally diverse regions like India's North East, where development interventions can disproportionately affect sectors. Furthermore, the spatial development theory (Friedmann, 1966) suggests that national averages often mask regional disparities, and thus, disaggregated analyses down to the district or sub-district level are essential for equitable and inclusive policy design (Barca et al., 2012). Similarly, Saha et al. (2023) emphasised that sustainable development is essential for balanced development in Assam.

Despite India's visible strides in advancing the SDGs, academic inquiry remains disproportionately anchored at the state level. However, the micro-level variations at the district scale, where disparities are often most pronounced, have rarely been explored in the extant literature. Recent studies emphasise the synergies and trade-offs across different goals, such as complementarities in health, gender equality and education, vis-à-vis tensions with industrial growth and energy use (Miola and Schiltz, 2019; Singh and Singh, 2021; Sinha et al., 2022). Moreover, the reliance on aggregated indicators obscures localised disparities that are especially salient in ecologically fragile and socio-culturally diverse regions like India's North East. Studies have underscored governance capacity, institutional quality and infrastructural constraints as key determinants of SDG achievement (Kumar et al., 2021; Rao and Kumar, 2022), yet most empirical works remain confined to interstate comparisons, neglecting intra-state heterogeneity (Deka and Nath, 2023; Sarkar and Sinha, 2023). Even macro-level regional studies such as Debnath et al. (2025) provide valuable descriptive insights but fail to capture district-level variations, thereby limiting their explanatory power. This gap is striking given that district-level disparities often determine the feasibility of inclusive and sustainable development in practice (Le Blanc, 2022; Sachs et al., 2022a, b). As a result, deviating from prior literature, the present study contributes by undertaking a district-level analysis of SDG performance in the NER of India, integrating governance, socio-economic and ecological dimensions to build an explanatory framework beyond descriptive diagnostics.

Therefore, the following research questions are raised in the present study-

1. To what extent has progress in SDG achievement been made in NER during the study period?

2. Are there any disparities among NER in achieving SDG during the study period?

The data methods employed in this study are designed to provide a comprehensive, multi-dimensional analysis of the developmental landscape across the NER. The methodology transitions from descriptive visualisation to rigorous inferential testing to ensure that the findings are both intuitive and statistically sound. This study undertakes a comprehensive analysis of the progress of the SDGs across 121 districts in the eight NER of India, spanning the period from 2021-22 to 2023-24. The NER of India comprises eight states, viz, Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura, commonly known as Ashtalakshmi, represents a geographically unique and socio-culturally diverse area with strategic importance. Despite its ecological wealth and demographic significance, the region lags behind the national average in several developmental dimensions, including in the SDG Score.

The methodological framework of this study is grounded in the necessity for a granular and statistically robust evaluation of development in a region defined by its unique ecological richness, cultural diversity and strategic geopolitical significance. Central to the research design is the shift from state-level monitoring to a district-level analysis of 121 districts across the eight North-Eastern States. This choice is justified by the theoretical principles of spatial development, which suggest that aggregated state-level indices often mask deep intra-regional disparities and leave remote districts in a developmental shadow. By utilising the district as the primary unit of observation, the study provides the first empirical examination capable of uncovering localised development gaps that are frequently overlooked in centralised planning.

Furthermore, the sample size of 121 districts – out of 131 total districts in the region – is specifically justified by data integrity requirements. These districts were selected because they met the required 80% data availability threshold established by NITI Aayog to be included in the final rankings. This sample size provides sufficient data points to ensure the findings are statistically sound and objective, allowing for rigorous inferential testing, such as independent-sample T-tests to verify temporal progress and one-way ANOVA to evaluate the statistical significance of interstate and inter-district performance variations. Ultimately, this granular approach supplements state-level indices by providing a more detailed understanding of spatial disparities and regional performance.

This study employs a quantitative research approach by utilising secondary data sources to achieve the stated research objectives. The analysis is based on annualised data for 2021-22 and 2023-24. The selection of this study period (2021-22 and 2023-24) is influenced by two primary considerations: data availability and analytical significance. This timeframe represents the most recent period for which comprehensive district-level SDG data from NITI Aayog are available, which serve as the official national framework for tracking sustainable development progress in India. Furthermore, this period is critical for capturing the region's post-pandemic developmental trajectory and identifying how adaptive governance mechanisms have functioned during the COVID-19 recovery phase. The selected period facilitates an examination of SDG achievement patterns and inter-district disparities during the COVID-19 recovery phase, contributing to the scholarly understanding of sustainable development challenges and adaptive governance mechanisms in India's NER.

This new global development agenda, also known as the Global Goals, comprising 17 goals and 169 targets, replaces the Millennium Development Goals (MDGs) adopted in 2000 and aims to be achieved by 2030 (Breuer et al., 2019; Mitra and Chatterjee, 2020).

The NES's District-level SDG Index serves as a strategic tool to monitor and accelerate development across the eight North Eastern States of India. By ranking districts based on their relative performance across 15 SDGs, the index aims to identify critical performance gaps, highlight inter-state disparities and foster a spirit of competitive federalism. This collaborative platform enables districts to share best practices and helps governments pinpoint sectors where robust data collection systems need to be instituted. The index is guided by the National Indicator Framework and involves 84 indicators selected through extensive consultation with state departments. To ensure data integrity, indicators were only included if they were technically sound, aligned with official statistical systems and had at least 70% data coverage across the districts.

The methodology for calculating the index involves a rigorous process of target setting and normalization to ensure all indicators are comparable. Targets for 2030 are determined using national standards, global SDG frameworks or benchmarks derived from top-performing districts. Because raw data ranges vary significantly – such as comparing mortality rates to percentages – values are normalized onto a standard scale of 0–100, where 100 represents target achievement. For indicators where higher values indicate better results, the following formula is used:

Where, x = raw data value

• min(x) = minimum observed value of the indicator in the dataset

• T(x) = target value for the indicator

• x’ = normalised value after rescaling

For indicators where higher values imply lower performance, such as infant mortality, the index utilizes an inverse formula:

Where, x = raw data value

• max(x) = maximum observed value of the indicator in the dataset

• T(x) = target value for the indicator

• x’ = normalised value after rescaling

Where the districts had achieved beyond the target set, the normalised score was capped at 100.

Once normalized, scores are aggregated to provide a clear picture of district-level progress. The specific score for each individual Goal is calculated as the arithmetic mean of the normalized indicators within that Goal, with each indicator receiving equal weight. The formula for the Goal score is:

Where Iij = Goal score for district i under SDG j

• Nij = number of non-null indicators for district I under SDGj

• Iijk = normalised value for district i of indicator k under SDGj

A composite score is then generated for every district by averaging these Goal scores. The composite score is calculated using the formula:

Where Ii = composite SDG index score of district i

• Ni = number of Goal scores for which district I has non-null data

• Iij = goal score for district i under SDG j

• Iijk = normalised value for district i of indicator k under SDG j

Certain exclusions apply to maintain accuracy: Goal 14 is omitted as it pertains to marine ecosystems, Goal 17 is excluded due to a lack of district-level indicators and Goal 11 is not factored into the composite score because it only applies to districts with Urban Local Bodies. Out of 131 total districts, 121 met the required 80% data availability threshold to be included in the final ranking. To simplify the assessment of progress, districts are classified into four categories: Achiever (score of 100), Front Runner (65–99), Performer (50–64) and Aspirant (less than 50).

The study relies on secondary data manually collected from the district-level SDG Index, computed by NITI Aayog (The premier policy thinktank of the Government of India and the nodal agency tasked with catalysing economic development, fostering cooperative federalism and moving away from bargaining federalism through the involvement of State Governments of India in the economic policy-making process using a bottom-up approach. The Aayog was established in 2015 to replace the Planning Commission, which followed a top-down model) published as the SDG-NER Report. The present study covers 121 districts across the NER and is based on their performance on the 15 SDGs. Given data availability, the study considers two years: 2021-22 and 2023-24.

The study is based on district-level secondary data on SDG performance. To ensure the findings are both objective and rigorous, the study employs a mix of descriptive and inferential statistical methodologies. For descriptive statistics, we employed a box plot. The primary use of a box plot is to summarise and visualise the distribution of numerical data in a compact format. It helps identify the dataset's central tendency (median), variability through the Interquartile Range (IQR) and outliers. Moreover, boxplots are non-parametric and don't assume a specific data distribution. This makes them especially useful in early-stage analysis. We used a boxplot to demonstrate the distribution pattern and detect outliers of SDG across 121 districts in the NER during 2021-22 and 2023-24. A spatial map is prepared for both years under consideration to demonstrate the SDG performance, visualising the location of the improvement or lagging. The use of boxplots and spatial distribution maps is justified as an essential diagnostic step for visualising central tendencies, variability and the physical locations of “lagging clusters” versus “pockets of excellence”.

To move beyond mere descriptive diagnostics, the study integrates T-tests to verify if progress in SDG scores represents a genuine upward trend rather than random variation. The t-test serves as a foundational diagnostic to validate the effectiveness of the SDG agenda in the region, providing the statistical rigour necessary for evidence-based policymaking. To investigate the improvement in the SDG performance during the study period, we used a t-test. The use of the independent-sample t-test is statistically justified as a robust inferential tool to determine whether the observed increase in the mean SDG score across the NER represents a significant evolutionary progress or mere stochastic variation. This methodology is particularly appropriate for this study because it allows for a direct comparison of the region's performance across two distinct time intervals – the 2021–22 and 2023–24 periods – thereby capturing the post-pandemic developmental trajectory.

Additionally, the application of one-way ANOVA is justified as the primary tool for examining the statistical significance of interstate and inter-district disparities, providing a data-driven foundation for state-specific policy recalibration and inclusive growth strategies. A one-way ANOVA test is utilised to examine interstate disparities in achieving SDG goals. Similarly, inter-district (within the state) disparities regarding SDG achievement are also analysed using a one-way ANOVA. Furthermore, independent-sample t-tests are conducted to evaluate the statistical significance of mean differences in SDG achievement throughout the study period.

By conducting these tests, the study moves beyond a descriptive summary to provide empirical validation that targeted interventions and adaptive governance mechanisms are yielding measurable results.

Figure 1 depicts the goal-wise performance of districts in North-East Indian states in 2023-24. In Figure 1, it is visible that around 85% of the districts of the NER are in the Front Runner category (overall score 65–99) compared to the previous edition, where 62% of the districts were in this category (See NITI Aayog Report 2021-22).

Figure 2 from NITI Aayog's SDG India Index 2023–24 highlights the SDG performance of NER between 2021–22 and 2023–24. The scores, expressed as percentages, reflect each state's progress toward achieving the 2030 targets. The figure shows a positive trend across most NES, with several recording notable improvements in their overall SDG scores. States like Meghalaya, Mizoram and Nagaland have made significant strides, moving into higher performance categories, which suggests that targeted development programs and improved governance mechanisms are beginning to yield results. However, Assam, Arunachal Pradesh and Manipur are still making slow progress.

While all districts of Mizoram, Sikkim and Tripura have advanced into the Front-Runner category (scores between 65 and 99), the other five states of the region still have some districts in the Performer category (scores between 50 and 64), indicating uneven progress. Therefore, the figure reveals regional disparities, underscoring the importance of region-specific strategies. The North-East requires sustained connectivity, human capital and institutional capacity investments. Strengthening local governance, enhancing data systems for monitoring and fostering community participation can accelerate SDG achievement.

The spatial maps from NITI Aayog's reports for 2021–22 and 2023–24 offer a compelling visual narrative of how districts across the NER of India are progressing toward the SDG (See Figure 3). These maps classify each district into four categories based on their SDG performance scores: Aspirant (0–49), Performer (50–64), Front Runner (65–99) and Achiever (100).

In the 2021–22 map, a significant number of districts fell into the Aspirant and Performer categories, indicating early-stage development and the need for targeted interventions. States like Sikkim and Mizoram had relatively better-performing districts, while others like Assam and Arunachal Pradesh showed mixed results, with several districts still striving to meet basic SDG benchmarks.

By 2023–24, the landscape had notably improved. Many districts transitioned into higher performance categories, with a visible increase in Front Runners and a few emerging Achievers. This shift reflects enhanced policy implementation, better resource allocation and stronger local governance. The maps also underscore persistent regional disparities – some districts continue to lag, especially in areas related to poverty, education and infrastructure.

The boxplot comparing SDG scores at the district level for 2021–22 and 2023–24 offers a clear visual representation of how performance has evolved, highlighting the central tendency, spread and variability of scores across these two periods (Figure 4). In 2021–22, the SDG distribution appears more compact, suggesting relatively consistent performance among the districts. The median score is slightly lower and the IQR is narrower, indicating less variation between the top and bottom performers.

In contrast, the 2023–24 boxplot shows a noticeable shift in the median score, indicating an overall improvement in SDG performance. However, this progress is accompanied by greater variability, as evidenced by a wider IQR and longer whiskers. This suggests that while some districts have made significant progress, others still lag behind. The presence of new outliers in 2023–24 could reflect exceptional cases – either those that have excelled or those that have fallen behind. Therefore, the boxplot presented in Figure 3 underscores both the gains and the disparities in SDG achievement over time. It is a useful diagnostic tool for policymakers and researchers to identify where interventions are working and where further attention is needed.

To investigate the statistically significant improvement in SDG score between 2021-22 and 2023-24, we employed two two-sample t-tests and presented the summary of the test in Table 1.

The t-test analysis comparing SDGs performance in NER of India between 2021–22 and 2023–24 reveals a statistically significant improvement (Table 1). The mean SDG score increased from 52.28 in 2021–22 to 65.14 in 2023–24, marking a notable rise of approximately 12.86 points. This difference is supported by a t-value of 4.2758 and a p-value of 0.0000, indicating that the improvement is not due to random variation but reflects a genuine upward trend in SDG performance. The result is significant at the 5% level, affirming the robustness of the findings.

This improvement suggests that the NER have made meaningful progress in implementing the SDG agenda over the past two years. The reduction in standard deviation from 27.80 to 17.93 also points to greater consistency in performance across states, implying that disparities in development outcomes may be narrowing. Such progress could be attributed to targeted interventions, increased central and state-level funding and enhanced monitoring mechanisms. It may also reflect growing awareness and capacity among local governments and civil society to align development efforts with the SDG framework.

Table 2 presents the summary of the ANOVA test conducted on the SDG scores of NER for the year 2021–22. The ANOVA result with an F-value of 1.79 and a p-value of 0.0970 indicates no statistically significant variation in performance across the states. This suggests that, while there may be observable differences in scores, they are not statistically meaningful and could be attributed to random variation rather than systemic disparities. Further, Bartlett's test for equal variances yielded a chi-square value of 1.5921 with a p-value of 0.9863, confirming that the assumption of homogeneity of variances holds true. This validates the use of ANOVA and reinforces the conclusion that the SDG score distribution is relatively uniform across the region.

In Table 3, the ANOVA test conducted on the SDG scores of NER for 2023–24 reveals a statistically significant variation in performance across the states. The F-value of 2.28 and a p-value of 0.0326 indicate that the differences in SDG scores among the eight states are significant at the 5% level. Bartlett's test for equal variances was conducted to validate the assumptions of ANOVA. The test yielded a chi-square value of 1.1126 with a p-value of 0.7734, indicating the assumption of homogeneity of variances holds. This confirms that the ANOVA results are statistically reliable and not biased due to unequal variances across groups.

In contrast to the previous year, this result suggests that some states have progressed more than others in achieving SDG targets. Table 4 presents the ANOVA results for SDGs across NER in India for 2021–22 and 2023–24. The analysis reveals statistically significant disparities in goal-wise performance, as indicated by consistently low Prob > F values (all below 0.05). The ANOVA results for SDGs across NER of India for 2021–22 and 2023–24 offer a nuanced picture of regional disparities and evolving development patterns. Each goal shows statistically significant variation across states in both years, as indicated by the consistently low p-values (Prob > F), all below the 0.05 threshold. This confirms that performance on each SDG differs meaningfully from state to state, warranting closer scrutiny of regional policy effectiveness.

The F-values, which measure the extent of variation among states, reveal important trends. For instance, Goal 1 (No Poverty) saw a sharp decline in F-value from 19.96 to 5.58, suggesting that poverty-related outcomes became more uniform across states – possibly due to centralised welfare schemes such as MGNREGA (Mahatma Gandhi National Rural Employment Guarantee Act) is a social security scheme that guarantees 100 days of wage employment to every rural household in India whose adult members volunteer for unskilled manual work. Similarly, Goal 2 (Zero Hunger) dropped from 32.33 to 2.65, indicating a significant reduction in disparity, perhaps reflecting successful food security programs or nutritional interventions like the National Food Security Act (NFSA), which provides subsidised food grains to nearly two-thirds of India's population and the Public Distribution System (PDS) ensures the supply of essential food items at subsidised rates to vulnerable sections. These findings corroborate the arguments of Bhattacharya (2024), who emphasised that policy intervention is an important catalyst for the development of the NER of India.

Conversely, some goals experienced rising F-values, pointing to growing divergence. Goal 4 (Quality Education) increased from 18.97 to 22.67 and Goal 6 (Clean Water and Sanitation) surged from 14.56 to 41.12. These spikes suggest that while some states may have made substantial progress, others lag behind, highlighting uneven implementation or access to infrastructure and services.

These findings carry important policy implications. Goals with declining F-values and low Bartlett's chi-square scores – such as No Poverty and Zero Hunger – may reflect successful pan-regional interventions. Policymakers should analyse these successes to replicate effective strategies across other goals. On the other hand, rising disparities in areas like education, sanitation and urban development call for state-specific policy recalibration. Central schemes may need to be complemented with localised planning, capacity building and infrastructure investment tailored to each state's unique challenges.

Moreover, the persistent variance in goals like Industry, Innovation and Infrastructure (Goal 9) and Responsible Consumption (Goal 12) suggests that economic and environmental policies are not uniformly effective. This underscores the need for adaptive governance, where states are empowered to innovate and customise interventions while maintaining alignment with national objectives.

The results of this study show that North-East India is making clear and measurable progress toward the SDGs. Between the 2021–22 and 2023–24 periods, the average SDG score for the region's districts climbed significantly from 52.28 to 65.14. This shift is most visible in the fact that 85% of the districts have now reached the “Front Runner” category, a substantial increase from the 62% recorded just two years prior. This means that for the majority of the region, the 2030 targets are becoming increasingly attainable.

However, this progress is not distributed evenly. While states like Mizoram, Sikkim and Tripura have seen every single one of their districts move into the high-performing Front-Runner category, other states like Assam, Arunachal Pradesh and Manipur still have districts that are struggling in the lower “Performer” category. Interestingly, the data shows that while some problems like poverty (SDG 1) and hunger (SDG 2) are becoming more uniform across the region, other areas like education (SDG 4) and clean water (SDG 6) are seeing a widening gap between the top-performing districts and those falling behind.

These findings align with and expand upon several key themes in contemporary developmental research. The significant improvement in the region's overall trajectory supports the arguments of Sachs et al. (2022a, b), who emphasise that India's national commitment is a primary driver for global SDG success. The success observed in reducing disparities for poverty and hunger goals (SDGs 1 and 2) likely reflects the impact of centralised welfare initiatives like MGNREGA (Mahatma Gandhi National Rural Employment Guarantee Scheme) and the National Food Security Act, corroborating Bhattacharya's (2024) assertion that targeted policy interventions are essential catalysts for development in the NER.

Conversely, the rising disparities in education and sanitation (SDGs 4 and 6) echo the concerns raised by Bhattacharya (2024) regarding the “mismatch” between literacy and employability, and Biswas et al. (2024) regarding the persistent regional gaps in WASH services. Furthermore, the study's focus on district-level variations provides empirical evidence for Friedmann's (1966) Spatial Development Theory, which warns that national or state averages often mask the reality of localised deprivation. By identifying “lagging clusters” despite an overall positive trend, this study reinforces the necessity of “place-based” regional planning as advocated by Purkarthofer (2023) and Barca et al. (2012) to ensure that the North-East's progress is truly inclusive and leaves no district behind.

The significant improvement in SDG performance across the NER between 2021-22 and 2023-24 is primarily attributable to a combination of centralised welfare initiatives and enhanced localised governance. The study suggests that the successful implementation of pan-regional schemes, such as MGNREGA for wage employment and the National Food Security Act (NFSA) for essential food distribution, played a pivotal role in narrowing gaps in “No Poverty” (SDG 1) and “Zero Hunger” (SDG 2). Furthermore, the transition of 85% of districts into the “Front Runner” category indicates a strengthening of institutional capacity and the adoption of adaptive governance mechanisms during the post-pandemic recovery phase. This upward trajectory was bolstered by increased central and state funding, improved monitoring systems and a heightened awareness among local governments and civil society, all anchored in India's national commitment to the 2030 Agenda.

Despite this overall progress, the study identifies distinct reasons for the continued variations in performance across different states and SDG parameters. While some goals showed converging trends, others, like “Quality Education” (SDG 4) and “Clean Water and Sanitation” (SDG 6), experienced rising disparities, suggesting uneven infrastructure investment and resource allocation. Performance variations across SDG parameters in the NER are driven by a combination of governance capacity, infrastructure constraints and the effectiveness of localised policy implementation. While the region has shown an overall significant upward trend in its developmental trajectory from 2021-22 to 2023-24, this progress is highly uneven across states and districts.

One major reason for these variations is the differential impact of centralised versus localised policy interventions. For goals like “No Poverty” (SDG 1) and “Zero Hunger” (SDG 2), performance across states has become more uniform over time, likely due to the successful implementation of pan-regional schemes such as MGNREGA and the National Food Security Act (NFSA). Conversely, goals such as “Quality Education” (SDG 4) and “Clean Water and Sanitation” (SDG 6) have seen rising disparities, indicating that while some states have made substantial infrastructure and service improvements, others continue to lag due to uneven resource allocation or institutional gaps.

Geographical and institutional factors also play a critical role. For instance, states like Mizoram, Sikkim and Tripura have successfully transitioned all their districts into the high-performing “Front Runner” category, while others like Assam, Arunachal Pradesh and Manipur face slower progress due to challenges such as geographical isolation and inadequate healthcare access in rugged terrains. Furthermore, the study identifies a “mismatch” between high literacy rates and actual employability in some districts, particularly those lacking digital infrastructure and vocational training, which impacts the achievement of education-related goals. These complex interdependencies mean that national or state-level averages often mask deep intra-regional disparities, necessitating more targeted, place-based policy recalibrations to ensure inclusive growth across the entire region.

The empirical analysis of the 121 districts in North-East India (NEI) confirms that the region has undergone a significant and statistically valid upward shift in its developmental trajectory. The rise in the mean SDG score from 52.28 in 2021–22 to 65.14 in 2023–24, backed by a significant t-test result (p < 0.05). It demonstrates that the localisation of SDG is yielding measurable results. The transition of 85% of districts into the “Front Runner” category suggests that the framework for sustainable development is no longer just a policy ideal but a functional reality across much of the North-Eastern frontier. However, the most critical conclusion drawn from the current research is that overall regional progress does not equate to localised equity. The ANOVA results reveal that while some goals (like Poverty and Hunger) are seeing a convergence in performance, others (like Quality Education and Clean Water and Sanitation) are characterised by widening disparities. From a policy perspective, these findings carry important implications. First, they validate the effectiveness of recent development strategies and suggest that continued investment in SDG-aligned programs can yield measurable results. Policymakers should consider scaling up successful initiatives, particularly those that address lagging indicators such as health, education and infrastructure. Second, the data underscores the need for sustained support to maintain momentum. While the improvement is encouraging, the average score of 65.14 still leaves room for further advancement toward the 2030 targets.

Moreover, the results highlight the importance of regional tailoring in policy design. The NER face unique geographic, cultural and economic challenges that require context-specific solutions. Strengthening local governance, improving connectivity and fostering inclusive growth should remain central to the SDG strategy in this region. Finally, the statistically significant improvement provides a strong case for integrating SDG performance metrics into routine policy evaluation frameworks, ensuring that progress is tracked transparently and used to inform future planning.

​To strengthen the policy framework for the Sustainable Development Goals in North-East India, the study advocates a strategic shift from uniform regional planning to a “place-based” approach that prioritises identifying and supporting lagging clusters within districts. This orientation requires a state-specific recalibration of policies, particularly to address widening disparities in Quality Education (SDG 4) and Clean Water and Sanitation (SDG 6), where interventions must move beyond basic access to focus on the quality and sustainability of service delivery. Furthermore, the region's unique geographical challenges necessitate sustained investment in resilient infrastructure and digital connectivity to bridge the developmental shadow that isolates remote terrains from mainstream economic progress. Central to this strategy is empowering local governance and integrating granular, district-level data into routine monitoring systems, ensuring that policy interventions are both evidence-based and culturally adaptive. By fostering institutional capacity and community-driven initiatives, policymakers can better align localised developmental needs with the broader national commitment to the 2030 Agenda, ultimately ensuring that no district is left behind in the region's upward trajectory of development.

The scope for future research emerging from this study is multifaceted and calls for a deeper analytical and methodological expansion. First, future studies should move beyond the mere identification of “pockets of excellence,” such as high-performing districts in Mizoram and Sikkim, to undertake rigorous causal analyses that uncover the underlying governance mechanisms, institutional arrangements and community-driven initiatives contributing to their superior performance. Second, longitudinal resilience studies are warranted to assess whether districts categorised as “Front Runners” in the 2023–24 baseline can sustain their developmental trajectory or experience stagnation as they approach the “Achiever” threshold, thereby offering insights into the dynamics of development plateaus. Third, given the significant inter-district disparities observed in Goal 4 (Quality Education) and Goal 6 (Clean Water and Sanitation), as evidenced by ANOVA results, there is a compelling need for goal-specific, mixed-methods investigations to explore the socio-cultural, institutional and infrastructural constraints affecting “Performer” districts, particularly in Assam and Arunachal Pradesh. Finally, in light of the region's increasing emphasis on digital transformation under the “Act East” policy framework, future research should incorporate digital readiness as a critical explanatory variable to evaluate its potential role in bridging intra-district disparities and accelerating progress toward SDGs.

ANOVA = 

Analysis of Variance

IQR = 

Interquartile Range

MGNREGA = 

Mahatma Gandhi National Rural Employment Guarantee Scheme

NER = 

North-East Region

NITI Aayog = 

National Institution for Transforming India Aayog

SDG = 

Sustainable Development Goals

UN = 

United Nations